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We describe both fully implicit methods based on the use of non-linear iteration solvers and semi-implicit methods based on the linearization of the coupling and on simpler linear solvers.
The simpler linear fusion technique for the partial results is their averaging.
High-level data representations provided by Deep Learning can be used for simpler linear models for Big Data.
In that case, KS smoothing or simpler linear approximation between the immediately lower and higher integer values can be applied depending on the available computational capacity.
However, much simpler, linear alternatives, such as the zero-forcing (ZF) receiver or the plain MMSE receiver ([11, 12]) are also popular because of their lower computational complexity.
Thus, the high-level complex data representations obtained by Deep Learning are useful for the application of computationally feasible and relatively simpler linear models for Big Data Analytics.
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Nor was it a simple linear increase.
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